A wavelet-based pattern recognition algorithm to classify postural transitions in humans

Elderly people can be monitored at home to detect autonomy issues in their behavior. In addition to the environmental sensors (presence and movements in a room, temperature in the flat, light, etc.), we developed an inertial- and magnetic-based acquisition board to monitor the activity of the person. This article presents a wavelet-based pattern recognition algorithm that works on the data of this acquisition board to detect the postural transitions occurring in the daily life. We constructed four patterns, one for each transition (between Sit and Stand and also Stand and Lying Down); to be able to detect them, and to infer the current posture. To test this algorithm and verify that the patterns are independent of the subject, we asked fifteen people to reproduce a scenario and we present, in the last section of this article, the results obtained. Results of an experiment are also given to show a mean good classification rate of 70% for this method.